






"""simple docstring"""


import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    is_torch_available,
    is_torch_tensor,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_torch_available():
		import torch

if is_vision_available():
		import PIL


__SCREAMING_SNAKE_CASE   =   logging.get_logger(__name__)


def        UpperCAmelCase      (					a__     ,      a__     ,      a__     ,      a__ ):





						'''simple docstring'''






						def constraint_to_multiple_of(a__     ,      a__     ,      a__=0     ,      a__=None ):
												lowerCAmelCase   :Dict						=			round(val / multiple ) * multiple

												if max_val is not None and x > max_val:
																		lowerCAmelCase   :int						=			math.floor(val / multiple ) * multiple

												if x < min_val:
																		lowerCAmelCase   :Dict						=			math.ceil(val / multiple ) * multiple

												return x

						lowerCAmelCase   :Optional[int]						=			(output_size, output_size) if isinstance(a__     ,      a__ ) else output_size

						lowerCAmelCase	, lowerCAmelCase   :Optional[Any]						=			get_image_size(a__ )
						lowerCAmelCase	, lowerCAmelCase   :List[str]						=			output_size

						# determine new height and width
						lowerCAmelCase   :Any						=			output_height / input_height
						lowerCAmelCase   :List[Any]						=			output_width / input_width

						if keep_aspect_ratio:
												# scale as little as possible
												if abs(1 - scale_width ) < abs(1 - scale_height ):
																		# fit width
																		lowerCAmelCase   :Union[str, Any]						=			scale_width
												else:
																		# fit height
																		lowerCAmelCase   :Union[str, Any]						=			scale_height

						lowerCAmelCase   :Optional[Any]						=			constraint_to_multiple_of(scale_height * input_height     ,      multiple=a__ )
						lowerCAmelCase   :List[str]						=			constraint_to_multiple_of(scale_width * input_width     ,      multiple=a__ )

						return (new_height, new_width)







class 						__UpperCamelCase     (     UpperCamelCase						):
					lowercase_    :				Any      =  ["""pixel_values"""]
					def __init__(							self							:	Dict     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Dict[str, int] = None     , UpperCAmelCase							:	PILImageResampling = PILImageResampling.BILINEAR     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Union[int, float] = 1 / 255     , UpperCAmelCase							:	bool = True     , UpperCAmelCase							:	Optional[Union[float, List[float]]] = None     , UpperCAmelCase							:	Optional[Union[float, List[float]]] = None     , **UpperCAmelCase							:	Optional[Any]     , )	->       None:
											super().__init__(**UpperCAmelCase   )
											lowerCAmelCase   :Any						=			size if size is not None else {'height': 384, 'width': 384}
											lowerCAmelCase   :Tuple						=			get_size_dict(UpperCAmelCase   )
											lowerCAmelCase   :List[str]						=			do_resize
											lowerCAmelCase   :str						=			size
											lowerCAmelCase   :str						=			keep_aspect_ratio
											lowerCAmelCase   :Union[str, Any]						=			ensure_multiple_of
											lowerCAmelCase   :Union[str, Any]						=			resample
											lowerCAmelCase   :Tuple						=			do_rescale
											lowerCAmelCase   :List[str]						=			rescale_factor
											lowerCAmelCase   :Optional[int]						=			do_normalize
											lowerCAmelCase   :Optional[int]						=			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											lowerCAmelCase   :List[str]						=			image_std if image_std is not None else IMAGENET_STANDARD_STD
					def        UpperCAmelCase__ (							self							:	Optional[Any]     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Dict[str, int]     , UpperCAmelCase							:	bool = False     , UpperCAmelCase							:	int = 1     , UpperCAmelCase							:	PILImageResampling = PILImageResampling.BICUBIC     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	List[str]     , )	->       np.ndarray:
											lowerCAmelCase   :List[str]						=			get_size_dict(UpperCAmelCase   )
											if "height" not in size or "width" not in size:
																	raise ValueError(f"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""   )
											lowerCAmelCase   :Any						=			get_resize_output_image_size(
											    UpperCAmelCase     , output_size=(size['height'], size['width'])     , keep_aspect_ratio=UpperCAmelCase     , multiple=UpperCAmelCase     , )
											return resize(UpperCAmelCase     , size=UpperCAmelCase     , resample=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	Union[str, Any]     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Union[int, float]     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	Union[str, Any]     , )	->       int:
											return rescale(UpperCAmelCase     , scale=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[Any]     , UpperCAmelCase							:	np.ndarray     , UpperCAmelCase							:	Union[float, List[float]]     , UpperCAmelCase							:	Union[float, List[float]]     , UpperCAmelCase							:	Optional[Union[str, ChannelDimension]] = None     , **UpperCAmelCase							:	List[str]     , )	->       np.ndarray:
											return normalize(UpperCAmelCase     , mean=UpperCAmelCase     , std=UpperCAmelCase     , data_format=UpperCAmelCase     , **UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	str     , UpperCAmelCase							:	ImageInput     , UpperCAmelCase							:	bool = None     , UpperCAmelCase							:	int = None     , UpperCAmelCase							:	bool = None     , UpperCAmelCase							:	int = None     , UpperCAmelCase							:	PILImageResampling = None     , UpperCAmelCase							:	bool = None     , UpperCAmelCase							:	float = None     , UpperCAmelCase							:	bool = None     , UpperCAmelCase							:	Optional[Union[float, List[float]]] = None     , UpperCAmelCase							:	Optional[Union[float, List[float]]] = None     , UpperCAmelCase							:	Optional[Union[str, TensorType]] = None     , UpperCAmelCase							:	ChannelDimension = ChannelDimension.FIRST     , **UpperCAmelCase							:	List[Any]     , )	->       PIL.Image.Image:
											lowerCAmelCase   :Tuple						=			do_resize if do_resize is not None else self.do_resize
											lowerCAmelCase   :List[str]						=			size if size is not None else self.size
											lowerCAmelCase   :Optional[int]						=			get_size_dict(UpperCAmelCase   )
											lowerCAmelCase   :Tuple						=			keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
											lowerCAmelCase   :str						=			ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
											lowerCAmelCase   :Optional[int]						=			resample if resample is not None else self.resample
											lowerCAmelCase   :int						=			do_rescale if do_rescale is not None else self.do_rescale
											lowerCAmelCase   :Tuple						=			rescale_factor if rescale_factor is not None else self.rescale_factor
											lowerCAmelCase   :Optional[Any]						=			do_normalize if do_normalize is not None else self.do_normalize
											lowerCAmelCase   :Optional[Any]						=			image_mean if image_mean is not None else self.image_mean
											lowerCAmelCase   :Optional[Any]						=			image_std if image_std is not None else self.image_std

											lowerCAmelCase   :Union[str, Any]						=			make_list_of_images(UpperCAmelCase   )

											if not valid_images(UpperCAmelCase   ):
																	raise ValueError(
																	    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
																	    'torch.Tensor, tf.Tensor or jax.ndarray.'   )

											if do_resize and size is None or resample is None:
																	raise ValueError('Size and resample must be specified if do_resize is True.'   )

											if do_rescale and rescale_factor is None:
																	raise ValueError('Rescale factor must be specified if do_rescale is True.'   )

											if do_normalize and (image_mean is None or image_std is None):
																	raise ValueError('Image mean and std must be specified if do_normalize is True.'   )

											# All transformations expect numpy arrays.
											lowerCAmelCase   :Tuple						=			[to_numpy_array(UpperCAmelCase   ) for image in images]

											if do_resize:
																	lowerCAmelCase   :Dict						=			[self.resize(image=UpperCAmelCase     , size=UpperCAmelCase     , resample=UpperCAmelCase   ) for image in images]

											if do_rescale:
																	lowerCAmelCase   :str						=			[self.rescale(image=UpperCAmelCase     , scale=UpperCAmelCase   ) for image in images]

											if do_normalize:
																	lowerCAmelCase   :List[Any]						=			[self.normalize(image=UpperCAmelCase     , mean=UpperCAmelCase     , std=UpperCAmelCase   ) for image in images]

											lowerCAmelCase   :int						=			[to_channel_dimension_format(UpperCAmelCase     , UpperCAmelCase   ) for image in images]

											lowerCAmelCase   :str						=			{'pixel_values': images}
											return BatchFeature(data=UpperCAmelCase     , tensor_type=UpperCAmelCase   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	Dict     , UpperCAmelCase							:	List[Tuple] = None   )	->       Tuple:
											lowerCAmelCase   :Dict						=			outputs.logits

											# Resize logits and compute semantic segmentation maps
											if target_sizes is not None:
																	if len(UpperCAmelCase   ) != len(UpperCAmelCase   ):
																							raise ValueError(
																							    'Make sure that you pass in as many target sizes as the batch dimension of the logits'   )

																	if is_torch_tensor(UpperCAmelCase   ):
																							lowerCAmelCase   :str						=			target_sizes.numpy()

																	lowerCAmelCase   :str						=			[]

																	for idx in range(len(UpperCAmelCase   )   ):
																							lowerCAmelCase   :str						=			torch.nn.functional.interpolate(
																							    logits[idx].unsqueeze(dim=0   )     , size=target_sizes[idx]     , mode='bilinear'     , align_corners=UpperCAmelCase   )
																							lowerCAmelCase   :int						=			resized_logits[0].argmax(dim=0   )
																							semantic_segmentation.append(UpperCAmelCase   )
											else:
																	lowerCAmelCase   :Tuple						=			logits.argmax(dim=1   )
																	lowerCAmelCase   :Optional[int]						=			[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]   )]

											return semantic_segmentation







"""simple docstring"""


import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey




class 						__UpperCamelCase     :
					def __init__(							self							:	Union[str, Any]     , UpperCAmelCase							:	Union[str, Any]     , UpperCAmelCase							:	int     , UpperCAmelCase							:	int   )	->       Tuple:
											if dst_width < 0 or dst_height < 0:
																	raise ValueError('Destination width/height should be > 0'   )

											lowerCAmelCase   :List[Any]						=			img
											lowerCAmelCase   :Optional[int]						=			img.shape[1]
											lowerCAmelCase   :Union[str, Any]						=			img.shape[0]
											lowerCAmelCase   :Optional[int]						=			dst_width
											lowerCAmelCase   :Optional[int]						=			dst_height

											lowerCAmelCase   :Any						=			self.src_w / self.dst_w
											lowerCAmelCase   :Dict						=			self.src_h / self.dst_h

											lowerCAmelCase   :int						=			(
											    np.ones((self.dst_h, self.dst_w, 3)     , np.uinta   ) * 255
											)
					def        UpperCAmelCase__ (							self							:	Union[str, Any]   )	->       List[str]:
											for i in range(self.dst_h   ):
																	for j in range(self.dst_w   ):
																							lowerCAmelCase   :Optional[Any]						=			self.img[self.get_y(UpperCAmelCase   )][self.get_x(UpperCAmelCase   )]
					def        UpperCAmelCase__ (							self							:	Optional[int]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_x * x   )
					def        UpperCAmelCase__ (							self							:	List[str]     , UpperCAmelCase							:	int   )	->       int:
											return int(self.ratio_y * y   )


if __name__ == "__main__":
		__SCREAMING_SNAKE_CASE,						__SCREAMING_SNAKE_CASE   =   8_00, 6_00
		__SCREAMING_SNAKE_CASE   =   imread('image_data/lena.jpg', 1)
		__SCREAMING_SNAKE_CASE   =   NearestNeighbour(im, dst_w, dst_h)
		n.process()

		imshow(
		    F"""Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}""", n.output
		)
		waitKey(0)
		destroyAllWindows()